Framing gender in Pakistan’s digital policies: a critical examination through capabilities and decolonial feminist lenses
Bibliographic record
Abstract
Purpose The authors problematize prevailing narratives surrounding gender in Pakistan’s digital policy landscape. Focused on challenging the dominant perspectives emanating from the Global North, our study seeks to contribute to the development of more inclusive policies. By applying the capabilities approach and decolonial feminism, they aim to investigate the intersectional vulnerabilities and gendered precarity perpetuated by the current framing of digital inclusion. This research endeavors to dismantle the simplistic notion of digital platforms as a universal remedy for gender equality and instead emphasizes the importance of context-sensitive and decolonial perspectives. Design/methodology/approach They adopt a combination of the capabilities approach and decolonial feminism to critically examine the portrayal of gender within Pakistan’s digital policy landscape, unraveling the underlying assumptions, discourses and ideologies shaping the gender gap framing in the context of digital inclusion. They treat policy documents as active agents shaping social reality, using content analysis to analyze the epistemic methods used in generating knowledge about the gender gap in digital inclusion. Findings Our study reveals that the gender gap framing in Pakistan’s digital inclusion is influenced by specific assumptions, discourses and ideologies embedded in policy documents. They shed light on how misrepresentation and instrumentalization contribute to intersectional vulnerabilities faced by women. Originality/value By treating policy documents as active agents shaping social reality, they provide a unique perspective on the gender gap in digital inclusion. This study challenges established narratives originating from the Global North and advocates for more inclusive policies. Our theoretical framework unveils the complexities of gender dynamics in digital platform work informing contextually positioned policymaking.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.020 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.021 | 0.067 |
| Scholarly communication | 0.018 | 0.015 |
| Open science | 0.001 | 0.012 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".